Core findings

  1. Regarding knowledge on a specialist and management level, the future will require even more intuitive IT interaction than before.  
  2. To keep up with the tempo of digitalization, it helps to implement a “fail fast” culture. This encourages eagerness to experiment and a positive way of dealing with mistakes.
  3. New competences are specifically required with a view to dealing with large quantities of data. In logistics, the data scientist has become an imperative career description.
  4. If the required qualifications are not met, it will be due to a lack of available resources rather than poor employee willingness.
  5. Digital competence screening can extrapolate a company’s specific qualification needs in a structured way.

Existing skills ...

... contain potential for enhancement

Challenges of further qualification

Future development of skills necessary …

… for skilled workers and managers

Possible methods of resolution

The Digital Competence Screening * - one approach to integrating skills

In the context of the digital competence screening the following questions have to be answered:

  • Step 1: How can data-based services can be determined and described?
  • Step 2: Which actions (e.g. programming) are necessary to provide data-based services?
  • Step 3: Which skills (e.g. programming, data warehousing) can be allocated to individual actions?
  • Step 4: Which skills exist actually, where is a lack of skills and which skills have to be integrated?
  • Step 5: Which measures can be taken to integrate the necessary skills?

* Source: Bayrle, C. (2017): Digital Competence Screening: Kompetenzen für datenbasierte Dienstleistungen identifizieren – Ein Handlungsleitfaden. IPRI Praxis Nr. 25, Stuttgart. Im Druck.

Numerous measures are possible

When buying in skills, companies (especially SME’s) have the possibility of using cloud solutions that are known as “as-a-service” concepts.These concepts are often based on pay-per-use models, and SMEs, in particular, can use these concepts to gain access to IT structures or IT services which they would otherwise have to integrate into their companies at great expense.* Alongside these services to resolve IT issues, there is another possible “as-a-service” concept which focuses on providing the skills of a data scientist: Data science as a service.There are numerous internal measures: Existing knowledge and skills can be used, employees can be trained in real-life scenarios and there is the possibility of cooperating with external partners, e.g. universities.